Overview

Dataset statistics

Number of variables41
Number of observations148532
Missing cells899080
Missing cells (%)14.8%
Total size in memory28.8 MiB
Average record size in memory203.1 B

Variable types

Numeric23
Categorical18

Alerts

admitdiagnosis has a high cardinality: 425 distinct values High cardinality
eyes is highly correlated with motor and 1 other fieldsHigh correlation
motor is highly correlated with eyes and 1 other fieldsHigh correlation
verbal is highly correlated with eyes and 1 other fieldsHigh correlation
creatinine is highly correlated with bunHigh correlation
bun is highly correlated with creatinineHigh correlation
eyes has 2290 (1.5%) missing values Missing
motor has 2290 (1.5%) missing values Missing
verbal has 2290 (1.5%) missing values Missing
urine has 72085 (48.5%) missing values Missing
wbc has 35064 (23.6%) missing values Missing
temperature has 6128 (4.1%) missing values Missing
sodium has 28687 (19.3%) missing values Missing
ph has 113595 (76.5%) missing values Missing
hematocrit has 31935 (21.5%) missing values Missing
creatinine has 29164 (19.6%) missing values Missing
albumin has 89683 (60.4%) missing values Missing
pao2 has 113595 (76.5%) missing values Missing
pco2 has 113595 (76.5%) missing values Missing
bun has 29727 (20.0%) missing values Missing
glucose has 16673 (11.2%) missing values Missing
bilirubin has 95114 (64.0%) missing values Missing
fio2 has 113595 (76.5%) missing values Missing
urine is highly skewed (γ1 = 41.44531748) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
meds has 146242 (98.5%) zeros Zeros
urine has 1674 (1.1%) zeros Zeros
age has 5223 (3.5%) zeros Zeros

Reproduction

Analysis started2022-03-29 02:19:21.197986
Analysis finished2022-03-29 02:20:59.874857
Duration1 minute and 38.68 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct148532
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148531
Minimum0
Maximum297062
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:20:59.963447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14853.1
Q174265.5
median148531
Q3222796.5
95-th percentile282208.9
Maximum297062
Range297062
Interquartile range (IQR)148531

Descriptive statistics

Standard deviation85755.27886
Coefficient of variation (CV)0.5773560998
Kurtosis-1.2
Mean148531
Median Absolute Deviation (MAD)74266
Skewness0
Sum2.206160649 × 1010
Variance7353967852
MonotonicityStrictly increasing
2022-03-29T11:21:00.147087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
1979961
 
< 0.1%
1980361
 
< 0.1%
1980381
 
< 0.1%
1980401
 
< 0.1%
1980421
 
< 0.1%
1980441
 
< 0.1%
1980461
 
< 0.1%
1980481
 
< 0.1%
1980501
 
< 0.1%
Other values (148522)148522
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
21
< 0.1%
41
< 0.1%
61
< 0.1%
81
< 0.1%
101
< 0.1%
121
< 0.1%
141
< 0.1%
161
< 0.1%
181
< 0.1%
ValueCountFrequency (%)
2970621
< 0.1%
2970601
< 0.1%
2970581
< 0.1%
2970561
< 0.1%
2970541
< 0.1%
2970521
< 0.1%
2970501
< 0.1%
2970481
< 0.1%
2970461
< 0.1%
2970441
< 0.1%

intubated
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
125766 
1
22766 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0125766
84.7%
122766
 
15.3%

Length

2022-03-29T11:21:00.312706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:00.392444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0125766
84.7%
122766
 
15.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

vent
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
112023 
1
36509 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0112023
75.4%
136509
 
24.6%

Length

2022-03-29T11:21:00.479634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:00.833393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0112023
75.4%
136509
 
24.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

dialysis
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
143042 
1
 
5490

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0143042
96.3%
15490
 
3.7%

Length

2022-03-29T11:21:00.922332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:01.010350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0143042
96.3%
15490
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

eyes
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)< 0.1%
Missing2290
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean3.48994133
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:01.084038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q34
95-th percentile4
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9326060591
Coefficient of variation (CV)0.2672268588
Kurtosis1.801692896
Mean3.48994133
Median Absolute Deviation (MAD)0
Skewness-1.757489537
Sum510376
Variance0.8697540614
MonotonicityNot monotonic
2022-03-29T11:21:01.208650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
4104312
70.2%
321930
 
14.8%
112662
 
8.5%
27338
 
4.9%
(Missing)2290
 
1.5%
ValueCountFrequency (%)
112662
 
8.5%
27338
 
4.9%
321930
 
14.8%
4104312
70.2%
ValueCountFrequency (%)
4104312
70.2%
321930
 
14.8%
27338
 
4.9%
112662
 
8.5%

motor
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing2290
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean5.48645396
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:01.323087image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median6
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.261229848
Coefficient of variation (CV)0.2298806947
Kurtosis6.663251734
Mean5.48645396
Median Absolute Deviation (MAD)0
Skewness-2.758574792
Sum802350
Variance1.590700729
MonotonicityNot monotonic
2022-03-29T11:21:01.435183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6116063
78.1%
512561
 
8.5%
18419
 
5.7%
47712
 
5.2%
3926
 
0.6%
2561
 
0.4%
(Missing)2290
 
1.5%
ValueCountFrequency (%)
18419
 
5.7%
2561
 
0.4%
3926
 
0.6%
47712
 
5.2%
512561
 
8.5%
6116063
78.1%
ValueCountFrequency (%)
6116063
78.1%
512561
 
8.5%
47712
 
5.2%
3926
 
0.6%
2561
 
0.4%
18419
 
5.7%

verbal
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing2290
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean4.020903708
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:01.536363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median5
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.538013786
Coefficient of variation (CV)0.3825045058
Kurtosis-0.1660480948
Mean4.020903708
Median Absolute Deviation (MAD)0
Skewness-1.249117853
Sum588025
Variance2.365486405
MonotonicityNot monotonic
2022-03-29T11:21:01.623321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
592970
62.6%
126101
 
17.6%
418814
 
12.7%
35104
 
3.4%
23253
 
2.2%
(Missing)2290
 
1.5%
ValueCountFrequency (%)
126101
 
17.6%
23253
 
2.2%
35104
 
3.4%
418814
 
12.7%
592970
62.6%
ValueCountFrequency (%)
592970
62.6%
418814
 
12.7%
35104
 
3.4%
23253
 
2.2%
126101
 
17.6%

meds
Real number (ℝ≥0)

ZEROS

Distinct2
Distinct (%)< 0.1%
Missing770
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.01028681258
Minimum0
Maximum1
Zeros146242
Zeros (%)98.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:01.730358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1009012535
Coefficient of variation (CV)9.808796727
Kurtosis92.22539715
Mean0.01028681258
Median Absolute Deviation (MAD)0
Skewness9.706912427
Sum1520
Variance0.01018106297
MonotonicityNot monotonic
2022-03-29T11:21:01.835351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
0146242
98.5%
11520
 
1.0%
(Missing)770
 
0.5%
ValueCountFrequency (%)
0146242
98.5%
11520
 
1.0%
ValueCountFrequency (%)
11520
 
1.0%
0146242
98.5%

urine
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct35577
Distinct (%)46.5%
Missing72085
Missing (%)48.5%
Infinite0
Infinite (%)0.0%
Mean1806.111998
Minimum0
Maximum269323.7472
Zeros1674
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:01.955677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105.4944
Q1783.648
median1443.5712
Q32404.944
95-th percentile4585.86144
Maximum269323.7472
Range269323.7472
Interquartile range (IQR)1621.296

Descriptive statistics

Standard deviation1858.128628
Coefficient of variation (CV)1.028800334
Kurtosis5651.346289
Mean1806.111998
Median Absolute Deviation (MAD)767.664
Skewness41.44531748
Sum138071843.9
Variance3452641.997
MonotonicityNot monotonic
2022-03-29T11:21:02.117619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01674
 
1.1%
1799.971250
 
< 0.1%
1439.942436
 
< 0.1%
2399.932836
 
< 0.1%
899.942436
 
< 0.1%
1199.923230
 
< 0.1%
2117.577629
 
< 0.1%
2057.097629
 
< 0.1%
1674.345629
 
< 0.1%
1090.886429
 
< 0.1%
Other values (35567)74469
50.1%
(Missing)72085
48.5%
ValueCountFrequency (%)
01674
1.1%
0.69125
 
< 0.1%
0.777617
 
< 0.1%
0.86413
 
< 0.1%
0.95049
 
< 0.1%
1.036813
 
< 0.1%
1.123212
 
< 0.1%
1.209612
 
< 0.1%
1.29611
 
< 0.1%
1.382412
 
< 0.1%
ValueCountFrequency (%)
269323.74721
< 0.1%
65063.60641
< 0.1%
39242.79361
< 0.1%
35636.88961
< 0.1%
33966.25921
< 0.1%
32180.5441
< 0.1%
30030.99841
< 0.1%
28544.91841
< 0.1%
27576.89281
< 0.1%
26954.72641
< 0.1%

wbc
Real number (ℝ≥0)

MISSING

Distinct3676
Distinct (%)3.2%
Missing35064
Missing (%)23.6%
Infinite0
Infinite (%)0.0%
Mean12.29150853
Minimum0.01
Maximum198.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:02.289889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile4.26
Q17.4
median10.4
Q315.2
95-th percentile25.3
Maximum198.1
Range198.09
Interquartile range (IQR)7.8

Descriptive statistics

Standard deviation8.186204531
Coefficient of variation (CV)0.6660048692
Kurtosis62.26266577
Mean12.29150853
Median Absolute Deviation (MAD)3.6
Skewness4.912691964
Sum1394692.89
Variance67.01394463
MonotonicityNot monotonic
2022-03-29T11:21:02.442400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.4900
 
0.6%
8.4890
 
0.6%
7.6882
 
0.6%
8.2875
 
0.6%
8874
 
0.6%
8.8871
 
0.6%
8.6864
 
0.6%
7.8863
 
0.6%
7857
 
0.6%
7.9854
 
0.6%
Other values (3666)104738
70.5%
(Missing)35064
 
23.6%
ValueCountFrequency (%)
0.011
 
< 0.1%
0.022
 
< 0.1%
0.031
 
< 0.1%
0.041
 
< 0.1%
0.071
 
< 0.1%
0.081
 
< 0.1%
0.091
 
< 0.1%
0.183
0.1%
0.114
 
< 0.1%
0.134
 
< 0.1%
ValueCountFrequency (%)
198.11
< 0.1%
189.71
< 0.1%
188.21
< 0.1%
186.21
< 0.1%
184.51
< 0.1%
183.61
< 0.1%
182.91
< 0.1%
179.31
< 0.1%
178.81
< 0.1%
177.81
< 0.1%

temperature
Real number (ℝ≥0)

MISSING

Distinct368
Distinct (%)0.3%
Missing6128
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean36.42329338
Minimum20
Maximum42.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:02.616908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile35.3
Q136.2
median36.5
Q336.7
95-th percentile37.3
Maximum42.3
Range22.3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.9318612237
Coefficient of variation (CV)0.02558421102
Kurtosis35.79314097
Mean36.42329338
Median Absolute Deviation (MAD)0.3
Skewness-2.632703372
Sum5186822.67
Variance0.8683653402
MonotonicityNot monotonic
2022-03-29T11:21:02.772287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.415191
 
10.2%
36.613913
 
9.4%
36.713083
 
8.8%
36.310666
 
7.2%
36.510086
 
6.8%
36.89392
 
6.3%
36.27485
 
5.0%
36.16974
 
4.7%
36.96346
 
4.3%
364909
 
3.3%
Other values (358)44359
29.9%
(Missing)6128
 
4.1%
ValueCountFrequency (%)
204
< 0.1%
20.21
 
< 0.1%
20.32
< 0.1%
20.42
< 0.1%
20.61
 
< 0.1%
20.83
< 0.1%
211
 
< 0.1%
21.13
< 0.1%
21.22
< 0.1%
21.41
 
< 0.1%
ValueCountFrequency (%)
42.31
 
< 0.1%
42.11
 
< 0.1%
421
 
< 0.1%
41.92
 
< 0.1%
41.831
 
< 0.1%
41.83
< 0.1%
41.73
< 0.1%
41.63
< 0.1%
41.55
< 0.1%
41.46
< 0.1%

respiratoryrate
Real number (ℝ≥0)

Distinct80
Distinct (%)0.1%
Missing960
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean25.58925745
Minimum4
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:02.929479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q111
median28
Q336
95-th percentile53
Maximum60
Range56
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.1207557
Coefficient of variation (CV)0.5909024806
Kurtosis-0.9258191621
Mean25.58925745
Median Absolute Deviation (MAD)14
Skewness0.2727098041
Sum3776257.9
Variance228.637253
MonotonicityNot monotonic
2022-03-29T11:21:03.101013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107208
 
4.9%
126919
 
4.7%
116521
 
4.4%
46026
 
4.1%
95823
 
3.9%
305150
 
3.5%
85126
 
3.5%
285084
 
3.4%
294786
 
3.2%
314525
 
3.0%
Other values (70)90404
60.9%
ValueCountFrequency (%)
46026
4.1%
53426
2.3%
5.92
 
< 0.1%
63442
2.3%
6.21
 
< 0.1%
6.81
 
< 0.1%
6.91
 
< 0.1%
73974
2.7%
7.12
 
< 0.1%
7.21
 
< 0.1%
ValueCountFrequency (%)
601541
1.0%
591044
0.7%
58822
0.6%
57788
0.5%
56833
0.6%
55817
0.6%
54835
0.6%
53798
0.5%
52917
0.6%
51880
0.6%

sodium
Real number (ℝ≥0)

MISSING

Distinct178
Distinct (%)0.1%
Missing28687
Missing (%)19.3%
Infinite0
Infinite (%)0.0%
Mean137.9672777
Minimum91
Maximum195
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:03.259771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum91
5-th percentile129
Q1135
median138
Q3141
95-th percentile146
Maximum195
Range104
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.569044952
Coefficient of variation (CV)0.04036496946
Kurtosis5.690864923
Mean137.9672777
Median Absolute Deviation (MAD)3
Skewness0.02455525409
Sum16534688.4
Variance31.01426168
MonotonicityNot monotonic
2022-03-29T11:21:03.417224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13812018
8.1%
13911785
 
7.9%
14011020
 
7.4%
13710953
 
7.4%
1369627
 
6.5%
1418946
 
6.0%
1357615
 
5.1%
1426967
 
4.7%
1346239
 
4.2%
1434692
 
3.2%
Other values (168)29983
20.2%
(Missing)28687
19.3%
ValueCountFrequency (%)
911
 
< 0.1%
981
 
< 0.1%
991
 
< 0.1%
1004
 
< 0.1%
1013
 
< 0.1%
1026
 
< 0.1%
1038
< 0.1%
1048
< 0.1%
1056
 
< 0.1%
10615
< 0.1%
ValueCountFrequency (%)
1951
 
< 0.1%
1941
 
< 0.1%
1861
 
< 0.1%
1842
 
< 0.1%
1813
 
< 0.1%
1802
 
< 0.1%
1797
< 0.1%
1782
 
< 0.1%
1778
< 0.1%
1765
< 0.1%

heartrate
Real number (ℝ≥0)

Distinct201
Distinct (%)0.1%
Missing360
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean100.5133763
Minimum20
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:03.553269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile48
Q187
median104
Q3120
95-th percentile147
Maximum220
Range200
Interquartile range (IQR)33

Descriptive statistics

Standard deviation30.99809929
Coefficient of variation (CV)0.3083977518
Kurtosis-0.2179306414
Mean100.5133763
Median Absolute Deviation (MAD)16
Skewness-0.2227696422
Sum14893268
Variance960.8821597
MonotonicityNot monotonic
2022-03-29T11:21:03.703460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1002928
 
2.0%
1022889
 
1.9%
1082851
 
1.9%
1042788
 
1.9%
1062762
 
1.9%
1102668
 
1.8%
982657
 
1.8%
1122637
 
1.8%
602582
 
1.7%
962575
 
1.7%
Other values (191)120835
81.4%
ValueCountFrequency (%)
2067
< 0.1%
2143
< 0.1%
2265
< 0.1%
2338
< 0.1%
2454
< 0.1%
2584
0.1%
2680
0.1%
2773
< 0.1%
2878
0.1%
2985
0.1%
ValueCountFrequency (%)
22010
< 0.1%
2195
 
< 0.1%
21813
< 0.1%
21711
< 0.1%
2166
< 0.1%
2157
< 0.1%
2147
< 0.1%
2137
< 0.1%
2125
 
< 0.1%
2115
 
< 0.1%

meanbp
Real number (ℝ≥0)

Distinct171
Distinct (%)0.1%
Missing481
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean86.88735139
Minimum40
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:03.892741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile42
Q153
median66
Q3124
95-th percentile164
Maximum200
Range160
Interquartile range (IQR)71

Descriptive statistics

Standard deviation41.88550115
Coefficient of variation (CV)0.4820667276
Kurtosis-0.7237969841
Mean86.88735139
Median Absolute Deviation (MAD)20
Skewness0.7474168084
Sum12863759.26
Variance1754.395207
MonotonicityNot monotonic
2022-03-29T11:21:04.052199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
563456
 
2.3%
543364
 
2.3%
403290
 
2.2%
553258
 
2.2%
583257
 
2.2%
603179
 
2.1%
533174
 
2.1%
573173
 
2.1%
523162
 
2.1%
593040
 
2.0%
Other values (161)115698
77.9%
ValueCountFrequency (%)
403290
2.2%
412533
1.7%
422386
1.6%
42.661
 
< 0.1%
432211
1.5%
442302
1.5%
452294
1.5%
462431
1.6%
472550
1.7%
47.661
 
< 0.1%
ValueCountFrequency (%)
200226
0.2%
199196
0.1%
198171
0.1%
197179
0.1%
196174
0.1%
195183
0.1%
194182
0.1%
193173
0.1%
192173
0.1%
191160
0.1%

ph
Real number (ℝ≥0)

MISSING

Distinct711
Distinct (%)2.0%
Missing113595
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean7.354385809
Minimum6.531
Maximum7.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:04.222194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.531
5-th percentile7.18
Q17.303
median7.36
Q37.42
95-th percentile7.5
Maximum7.81
Range1.279
Interquartile range (IQR)0.117

Descriptive statistics

Standard deviation0.1011323145
Coefficient of variation (CV)0.01375129306
Kurtosis2.928452011
Mean7.354385809
Median Absolute Deviation (MAD)0.06
Skewness-0.9227457947
Sum256940.177
Variance0.01022774504
MonotonicityNot monotonic
2022-03-29T11:21:04.421118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.381194
 
0.8%
7.361184
 
0.8%
7.371132
 
0.8%
7.351115
 
0.8%
7.41108
 
0.7%
7.341103
 
0.7%
7.331055
 
0.7%
7.391054
 
0.7%
7.41995
 
0.7%
7.32971
 
0.7%
Other values (701)24026
 
16.2%
(Missing)113595
76.5%
ValueCountFrequency (%)
6.5311
< 0.1%
6.6111
< 0.1%
6.6461
< 0.1%
6.711
< 0.1%
6.721
< 0.1%
6.7241
< 0.1%
6.7371
< 0.1%
6.741
< 0.1%
6.7431
< 0.1%
6.7631
< 0.1%
ValueCountFrequency (%)
7.811
 
< 0.1%
7.7781
 
< 0.1%
7.7151
 
< 0.1%
7.712
< 0.1%
7.7061
 
< 0.1%
7.7051
 
< 0.1%
7.74
< 0.1%
7.691
 
< 0.1%
7.6891
 
< 0.1%
7.6841
 
< 0.1%

hematocrit
Real number (ℝ≥0)

MISSING

Distinct546
Distinct (%)0.5%
Missing31935
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean32.60035121
Minimum6
Maximum72.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:04.594428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile21.5
Q127.6
median32.7
Q337.4
95-th percentile43.6
Maximum72.7
Range66.7
Interquartile range (IQR)9.8

Descriptive statistics

Standard deviation6.910806609
Coefficient of variation (CV)0.211985649
Kurtosis-0.0654282761
Mean32.60035121
Median Absolute Deviation (MAD)4.9
Skewness0.08829139132
Sum3801103.15
Variance47.75924799
MonotonicityNot monotonic
2022-03-29T11:21:04.768151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
341071
 
0.7%
331053
 
0.7%
321043
 
0.7%
351032
 
0.7%
361029
 
0.7%
311005
 
0.7%
29996
 
0.7%
30973
 
0.7%
28964
 
0.6%
37908
 
0.6%
Other values (536)106523
71.7%
(Missing)31935
 
21.5%
ValueCountFrequency (%)
61
< 0.1%
6.11
< 0.1%
6.61
< 0.1%
6.81
< 0.1%
7.11
< 0.1%
7.21
< 0.1%
7.32
< 0.1%
7.52
< 0.1%
7.71
< 0.1%
7.81
< 0.1%
ValueCountFrequency (%)
72.71
< 0.1%
671
< 0.1%
66.11
< 0.1%
65.31
< 0.1%
651
< 0.1%
64.41
< 0.1%
64.21
< 0.1%
63.61
< 0.1%
62.52
< 0.1%
62.41
< 0.1%

creatinine
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct1629
Distinct (%)1.4%
Missing29164
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean1.556179336
Minimum0.1
Maximum24.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:05.185571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.49
Q10.72
median1
Q31.6
95-th percentile4.75
Maximum24.95
Range24.85
Interquartile range (IQR)0.88

Descriptive statistics

Standard deviation1.730901466
Coefficient of variation (CV)1.112276346
Kurtosis25.20740521
Mean1.556179336
Median Absolute Deviation (MAD)0.35
Skewness4.1863051
Sum185758.015
Variance2.996019883
MonotonicityNot monotonic
2022-03-29T11:21:05.383317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.83880
 
2.6%
0.73754
 
2.5%
0.93019
 
2.0%
0.62914
 
2.0%
1.12033
 
1.4%
11964
 
1.3%
1.21787
 
1.2%
0.51780
 
1.2%
1.31539
 
1.0%
1.41287
 
0.9%
Other values (1619)95411
64.2%
(Missing)29164
 
19.6%
ValueCountFrequency (%)
0.113
< 0.1%
0.113
 
< 0.1%
0.126
 
< 0.1%
0.135
 
< 0.1%
0.146
 
< 0.1%
0.158
< 0.1%
0.167
< 0.1%
0.1715
< 0.1%
0.1814
< 0.1%
0.1911
< 0.1%
ValueCountFrequency (%)
24.951
< 0.1%
24.61
< 0.1%
24.31
< 0.1%
23.91
< 0.1%
23.871
< 0.1%
23.691
< 0.1%
23.61
< 0.1%
23.431
< 0.1%
23.31
< 0.1%
23.11
< 0.1%

albumin
Real number (ℝ≥0)

MISSING

Distinct53
Distinct (%)0.1%
Missing89683
Missing (%)60.4%
Infinite0
Infinite (%)0.0%
Mean2.871447433
Minimum1
Maximum7.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:05.621363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.7
Q12.4
median2.9
Q33.4
95-th percentile4
Maximum7.4
Range6.4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6949763648
Coefficient of variation (CV)0.24202998
Kurtosis-0.235014196
Mean2.871447433
Median Absolute Deviation (MAD)0.5
Skewness-0.02032262332
Sum168981.81
Variance0.4829921477
MonotonicityNot monotonic
2022-03-29T11:21:05.803013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.13310
 
2.2%
2.93295
 
2.2%
2.83285
 
2.2%
33278
 
2.2%
3.23098
 
2.1%
2.72986
 
2.0%
2.62946
 
2.0%
3.32861
 
1.9%
2.52755
 
1.9%
2.42542
 
1.7%
Other values (43)28493
 
19.2%
(Missing)89683
60.4%
ValueCountFrequency (%)
178
 
0.1%
1.1158
 
0.1%
1.2232
 
0.2%
1.3309
 
0.2%
1.4373
 
0.3%
1.5553
0.4%
1.6782
0.5%
1.7910
0.6%
1.81095
0.7%
1.91357
0.9%
ValueCountFrequency (%)
7.41
 
< 0.1%
6.61
 
< 0.1%
63
 
< 0.1%
5.72
 
< 0.1%
5.62
 
< 0.1%
5.52
 
< 0.1%
5.43
 
< 0.1%
5.35
< 0.1%
5.26
< 0.1%
5.18
< 0.1%

pao2
Real number (ℝ≥0)

MISSING

Distinct2321
Distinct (%)6.6%
Missing113595
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean130.2230529
Minimum9
Maximum636
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:05.944777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile53.38
Q176
median102
Q3153
95-th percentile317
Maximum636
Range627
Interquartile range (IQR)77

Descriptive statistics

Standard deviation85.09227384
Coefficient of variation (CV)0.6534347946
Kurtosis4.84552623
Mean130.2230529
Median Absolute Deviation (MAD)32
Skewness2.063884058
Sum4549602.8
Variance7240.695067
MonotonicityNot monotonic
2022-03-29T11:21:06.074942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75333
 
0.2%
76331
 
0.2%
82328
 
0.2%
70324
 
0.2%
78322
 
0.2%
77322
 
0.2%
80320
 
0.2%
79318
 
0.2%
69313
 
0.2%
71313
 
0.2%
Other values (2311)31713
 
21.4%
(Missing)113595
76.5%
ValueCountFrequency (%)
91
 
< 0.1%
151
 
< 0.1%
161
 
< 0.1%
173
< 0.1%
182
< 0.1%
18.51
 
< 0.1%
18.91
 
< 0.1%
193
< 0.1%
19.71
 
< 0.1%
202
< 0.1%
ValueCountFrequency (%)
6361
< 0.1%
6201
< 0.1%
6071
< 0.1%
6021
< 0.1%
601.61
< 0.1%
5991
< 0.1%
5971
< 0.1%
5871
< 0.1%
5821
< 0.1%
5791
< 0.1%

pco2
Real number (ℝ≥0)

MISSING

Distinct894
Distinct (%)2.6%
Missing113595
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean42.92129261
Minimum6.9
Maximum147.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:06.209814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile26.4
Q134.8
median40.8
Q348
95-th percentile69.1
Maximum147.3
Range140.4
Interquartile range (IQR)13.2

Descriptive statistics

Standard deviation13.38817544
Coefficient of variation (CV)0.311923864
Kurtosis4.844949752
Mean42.92129261
Median Absolute Deviation (MAD)6.6
Skewness1.620583199
Sum1499541.2
Variance179.2432417
MonotonicityNot monotonic
2022-03-29T11:21:06.341775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41932
 
0.6%
38931
 
0.6%
40869
 
0.6%
37856
 
0.6%
42846
 
0.6%
39833
 
0.6%
43807
 
0.5%
35791
 
0.5%
36779
 
0.5%
44704
 
0.5%
Other values (884)26589
 
17.9%
(Missing)113595
76.5%
ValueCountFrequency (%)
6.91
 
< 0.1%
71
 
< 0.1%
7.91
 
< 0.1%
81
 
< 0.1%
8.41
 
< 0.1%
91
 
< 0.1%
9.51
 
< 0.1%
9.71
 
< 0.1%
106
< 0.1%
10.21
 
< 0.1%
ValueCountFrequency (%)
147.31
< 0.1%
147.11
< 0.1%
145.81
< 0.1%
143.11
< 0.1%
1411
< 0.1%
139.81
< 0.1%
1392
< 0.1%
136.41
< 0.1%
1361
< 0.1%
1301
< 0.1%

bun
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct624
Distinct (%)0.5%
Missing29727
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean26.81385809
Minimum1
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:06.529069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q113
median19.6
Q333
95-th percentile71
Maximum254
Range253
Interquartile range (IQR)20

Descriptive statistics

Standard deviation22.08124449
Coefficient of variation (CV)0.8235012066
Kurtosis9.04493289
Mean26.81385809
Median Absolute Deviation (MAD)8.6
Skewness2.449010834
Sum3185620.41
Variance487.5813581
MonotonicityNot monotonic
2022-03-29T11:21:06.691294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135088
 
3.4%
144960
 
3.3%
154921
 
3.3%
124834
 
3.3%
114770
 
3.2%
164534
 
3.1%
104373
 
2.9%
174215
 
2.8%
183914
 
2.6%
93812
 
2.6%
Other values (614)73384
49.4%
(Missing)29727
20.0%
ValueCountFrequency (%)
119
 
< 0.1%
2114
 
0.1%
2.81
 
< 0.1%
2.91
 
< 0.1%
3343
0.2%
3.31
 
< 0.1%
3.41
 
< 0.1%
3.53
 
< 0.1%
3.61
 
< 0.1%
3.71
 
< 0.1%
ValueCountFrequency (%)
2542
< 0.1%
2531
 
< 0.1%
2521
 
< 0.1%
2491
 
< 0.1%
2482
< 0.1%
2441
 
< 0.1%
2383
< 0.1%
2371
 
< 0.1%
2351
 
< 0.1%
2331
 
< 0.1%

glucose
Real number (ℝ≥0)

MISSING

Distinct1000
Distinct (%)0.8%
Missing16673
Missing (%)11.2%
Infinite0
Infinite (%)0.0%
Mean163.5492943
Minimum1
Maximum2357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:06.829242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile72
Q196
median135
Q3199
95-th percentile348
Maximum2357
Range2356
Interquartile range (IQR)103

Descriptive statistics

Standard deviation101.6886959
Coefficient of variation (CV)0.621761753
Kurtosis18.90393743
Mean163.5492943
Median Absolute Deviation (MAD)45
Skewness2.973183003
Sum21565446.4
Variance10340.59088
MonotonicityNot monotonic
2022-03-29T11:21:06.985445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
921501
 
1.0%
961486
 
1.0%
951463
 
1.0%
991462
 
1.0%
971457
 
1.0%
941446
 
1.0%
931431
 
1.0%
981425
 
1.0%
911415
 
1.0%
1001392
 
0.9%
Other values (990)117381
79.0%
(Missing)16673
 
11.2%
ValueCountFrequency (%)
12
 
< 0.1%
34
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
65
 
< 0.1%
82
 
< 0.1%
95
 
< 0.1%
108
< 0.1%
119
< 0.1%
1214
< 0.1%
ValueCountFrequency (%)
23571
< 0.1%
17311
< 0.1%
16911
< 0.1%
16881
< 0.1%
16441
< 0.1%
15101
< 0.1%
14951
< 0.1%
14781
< 0.1%
14641
< 0.1%
14611
< 0.1%

bilirubin
Real number (ℝ≥0)

MISSING

Distinct628
Distinct (%)1.2%
Missing95114
Missing (%)64.0%
Infinite0
Infinite (%)0.0%
Mean1.222612602
Minimum0.1
Maximum60.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:07.126547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.7
Q31.1
95-th percentile3.6
Maximum60.2
Range60.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation2.441969554
Coefficient of variation (CV)1.997337136
Kurtosis103.3632117
Mean1.222612602
Median Absolute Deviation (MAD)0.3
Skewness8.605536724
Sum65309.52
Variance5.963215302
MonotonicityNot monotonic
2022-03-29T11:21:07.253603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.46370
 
4.3%
0.55996
 
4.0%
0.35361
 
3.6%
0.65007
 
3.4%
0.74165
 
2.8%
0.83423
 
2.3%
0.22679
 
1.8%
0.92618
 
1.8%
12188
 
1.5%
1.11644
 
1.1%
Other values (618)13967
 
9.4%
(Missing)95114
64.0%
ValueCountFrequency (%)
0.1332
 
0.2%
0.121
 
< 0.1%
0.131
 
< 0.1%
0.144
 
< 0.1%
0.154
 
< 0.1%
0.161
 
< 0.1%
0.175
 
< 0.1%
0.183
 
< 0.1%
0.192
 
< 0.1%
0.22679
1.8%
ValueCountFrequency (%)
60.21
< 0.1%
521
< 0.1%
51.21
< 0.1%
511
< 0.1%
50.91
< 0.1%
481
< 0.1%
46.41
< 0.1%
45.61
< 0.1%
44.81
< 0.1%
44.51
< 0.1%

fio2
Real number (ℝ≥0)

MISSING

Distinct89
Distinct (%)0.3%
Missing113595
Missing (%)76.5%
Infinite0
Infinite (%)0.0%
Mean59.27130549
Minimum21
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:07.461258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile25
Q140
median50
Q380
95-th percentile100
Maximum100
Range79
Interquartile range (IQR)40

Descriptive statistics

Standard deviation26.28973239
Coefficient of variation (CV)0.4435490694
Kurtosis-1.149465396
Mean59.27130549
Median Absolute Deviation (MAD)15
Skewness0.4990192879
Sum2070761.6
Variance691.1500293
MonotonicityNot monotonic
2022-03-29T11:21:07.673067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1008045
 
5.4%
406357
 
4.3%
506053
 
4.1%
603441
 
2.3%
301956
 
1.3%
211433
 
1.0%
801253
 
0.8%
701229
 
0.8%
35950
 
0.6%
28710
 
0.5%
Other values (79)3510
 
2.4%
(Missing)113595
76.5%
ValueCountFrequency (%)
211433
1.0%
228
 
< 0.1%
2330
 
< 0.1%
2465
 
< 0.1%
25454
 
0.3%
2648
 
< 0.1%
27153
 
0.1%
28710
 
0.5%
2927
 
< 0.1%
301956
1.3%
ValueCountFrequency (%)
1008045
5.4%
99.61
 
< 0.1%
9912
 
< 0.1%
98.81
 
< 0.1%
98.52
 
< 0.1%
98.41
 
< 0.1%
98.21
 
< 0.1%
9813
 
< 0.1%
97.31
 
< 0.1%
978
 
< 0.1%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing53
Missing (%)< 0.1%
Memory size145.3 KiB
0.0
80417 
1.0
68062 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.080417
54.1%
1.068062
45.8%
(Missing)53
 
< 0.1%

Length

2022-03-29T11:21:07.851709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:07.920214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.080417
54.2%
1.068062
45.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

age
Real number (ℝ≥0)

ZEROS

Distinct86
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.87528613
Minimum0
Maximum89
Zeros5223
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-03-29T11:21:08.007146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q151
median63
Q374
95-th percentile85
Maximum89
Range89
Interquartile range (IQR)23

Descriptive statistics

Standard deviation19.9254878
Coefficient of variation (CV)0.3327831746
Kurtosis1.078140002
Mean59.87528613
Median Absolute Deviation (MAD)12
Skewness-1.073219682
Sum8893396
Variance397.0250641
MonotonicityNot monotonic
2022-03-29T11:21:08.167391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05223
 
3.5%
673745
 
2.5%
683562
 
2.4%
713504
 
2.4%
723496
 
2.4%
653427
 
2.3%
663412
 
2.3%
703348
 
2.3%
633293
 
2.2%
623257
 
2.2%
Other values (76)112265
75.6%
ValueCountFrequency (%)
05223
3.5%
12
 
< 0.1%
42
 
< 0.1%
72
 
< 0.1%
82
 
< 0.1%
93
 
< 0.1%
102
 
< 0.1%
115
 
< 0.1%
127
 
< 0.1%
1311
 
< 0.1%
ValueCountFrequency (%)
891431
1.0%
881642
1.1%
871887
1.3%
862006
1.4%
852213
1.5%
842416
1.6%
832515
1.7%
822510
1.7%
812584
1.7%
802597
1.7%

admitdiagnosis
Categorical

HIGH CARDINALITY

Distinct425
Distinct (%)0.3%
Missing946
Missing (%)0.6%
Memory size309.7 KiB
SEPSISPULM
 
7352
AMI
 
6138
CVASTROKE
 
5564
CHF
 
5318
SEPSISUTI
 
4454
Other values (420)
118760 

Length

Max length10
Median length9
Mean length8.11530904
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowRHYTHATR
2nd rowSEPSISUTI
3rd rowRESPARREST
4th rowODSEDHYP
5th rowSEPSISPULM

Common Values

ValueCountFrequency (%)
SEPSISPULM7352
 
4.9%
AMI6138
 
4.1%
CVASTROKE5564
 
3.7%
CHF5318
 
3.6%
SEPSISUTI4454
 
3.0%
DKA4149
 
2.8%
S-CABG4043
 
2.7%
RHYTHATR3935
 
2.6%
CARDARREST3730
 
2.5%
EMPHYSBRON3660
 
2.5%
Other values (415)99243
66.8%

Length

2022-03-29T11:21:08.285663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sepsispulm7352
 
5.0%
ami6138
 
4.2%
cvastroke5564
 
3.8%
chf5318
 
3.6%
sepsisuti4454
 
3.0%
dka4149
 
2.8%
s-cabg4043
 
2.7%
rhythatr3935
 
2.7%
cardarrest3730
 
2.5%
emphysbron3660
 
2.5%
Other values (415)99243
67.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

aids
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
148370 
1
 
162

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0148370
99.9%
1162
 
0.1%

Length

2022-03-29T11:21:08.424190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:08.490200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0148370
99.9%
1162
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

hepaticfailure
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
146187 
1
 
2345

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0146187
98.4%
12345
 
1.6%

Length

2022-03-29T11:21:08.551858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:08.616189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0146187
98.4%
12345
 
1.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lymphoma
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
147868 
1
 
664

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0147868
99.6%
1664
 
0.4%

Length

2022-03-29T11:21:08.685912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:08.764837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0147868
99.6%
1664
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

metastaticcancer
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
145429 
1
 
3103

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0145429
97.9%
13103
 
2.1%

Length

2022-03-29T11:21:08.840428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:09.151453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0145429
97.9%
13103
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

leukemia
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
147410 
1
 
1122

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0147410
99.2%
11122
 
0.8%

Length

2022-03-29T11:21:09.248465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:09.333055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0147410
99.2%
11122
 
0.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
144442 
1
 
4090

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0144442
97.2%
14090
 
2.8%

Length

2022-03-29T11:21:09.410115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:09.489049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0144442
97.2%
14090
 
2.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

cirrhosis
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
145831 
1
 
2701

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0145831
98.2%
12701
 
1.8%

Length

2022-03-29T11:21:09.558556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:09.622448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0145831
98.2%
12701
 
1.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

electivesurgery
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
122360 
1
26172 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0122360
82.4%
126172
 
17.6%

Length

2022-03-29T11:21:09.707221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:09.808042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0122360
82.4%
126172
 
17.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

activetx
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
1
86249 
0
62283 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
186249
58.1%
062283
41.9%

Length

2022-03-29T11:21:09.887199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:09.971841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
186249
58.1%
062283
41.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

readmit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
140262 
1
 
8270

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0140262
94.4%
18270
 
5.6%

Length

2022-03-29T11:21:10.050162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:10.129740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0140262
94.4%
18270
 
5.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
113849 
1
34683 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0113849
76.6%
134683
 
23.4%

Length

2022-03-29T11:21:10.207091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:10.286350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0113849
76.6%
134683
 
23.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
139917 
1
 
8615

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0139917
94.2%
18615
 
5.8%

Length

2022-03-29T11:21:10.364764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:10.465353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0139917
94.2%
18615
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size145.3 KiB
0
134795 
1
13737 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0134795
90.8%
113737
 
9.2%

Length

2022-03-29T11:21:10.540341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-03-29T11:21:10.603093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0134795
90.8%
113737
 
9.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-03-29T11:20:50.757938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:23.270513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:27.118189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:31.166155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:35.045775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:39.386071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:43.194021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:46.462099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:50.097632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:54.063017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:58.626953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:03.010242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:07.389527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:11.515916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:14.460334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:17.736525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:21.411878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:25.595680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:29.863912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:34.167670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:38.838554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:42.833928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:47.418802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:50.955921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:23.473980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:27.261582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:31.340073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:35.423038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:39.543512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:43.352285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:46.596760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:50.249096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:54.245158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:58.817076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:03.449809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:07.572423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:11.684566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:14.594879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:17.865618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:21.577073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:25.750655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:30.029050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:34.406638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:38.991004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:42.994443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:47.561030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:51.134154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:23.634183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:27.436722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:31.499847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:35.581531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:39.710956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:43.489918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:46.731796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:50.399432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:54.477156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:58.981386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:03.655545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:07.753441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:11.832370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:14.727142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:18.008523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:21.732948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:25.922098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:30.195856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:34.668138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:39.144491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:43.183832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:47.712355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:51.271597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:23.806584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:27.600835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:31.684644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:35.719736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:39.888051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:43.627413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:46.870603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:50.538024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:54.724611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:59.154439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:03.828101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:07.929292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:11.989878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:14.854367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:18.164308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:21.860784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:26.073439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:30.351345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:34.922420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:39.307615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:43.392732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:47.855162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:51.677231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:23.999495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:27.785449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:31.885422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:35.934687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:40.086458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:43.776340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:47.051632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:50.696891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:54.969292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:59.336285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:04.045907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:08.128335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:12.171352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:14.996133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:18.336258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:22.071410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:26.245711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:30.507545image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:35.172099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:39.519447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:43.644577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:48.039963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:51.800045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:24.165524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:27.962638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:32.044418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:36.098415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:40.267506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:43.909503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:47.189379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:50.835737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:55.162313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:59.498016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:04.251917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:08.311773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:12.306318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:15.123145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:18.474266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:22.492307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:26.418624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:30.647230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:35.327755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:39.695413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:43.811945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:48.191064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:51.930992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:24.325648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:28.328462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:32.206711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:36.275370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:40.415604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:44.057309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:47.333856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:50.989282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:55.634384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:59.666490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:04.451841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:08.486687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:12.436472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:15.260737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:18.642932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:22.659237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:26.570803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:30.853139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:35.496371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:39.853901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:43.961487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:48.349742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:52.114765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:24.489269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:28.526453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:32.384402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:36.470118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:40.600797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:44.207105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:47.498420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:51.152525image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:19:55.829517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-29T11:19:45.516288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-29T11:19:45.802151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-29T11:19:57.841691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:02.051056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-29T11:20:13.863547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-29T11:19:42.590758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-29T11:19:53.360844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-03-29T11:20:47.290218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-03-29T11:20:50.595126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-03-29T11:21:10.692211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Missing values

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A simple visualization of nullity by column.
2022-03-29T11:20:56.514848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-03-29T11:20:58.803615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-03-29T11:20:59.673313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexintubatedventdialysiseyesmotorverbalmedsurinewbctemperaturerespiratoryratesodiumheartratemeanbpphhematocritcreatininealbuminpao2pco2bunglucosebilirubinfio2genderageadmitdiagnosisaidshepaticfailurelymphomametastaticcancerleukemiaimmunosuppressioncirrhosiselectivesurgeryactivetxreadmitdiabetesactualicumortalityactualhospitalmortality
000004.06.05.00.0NaN14.736.130.0139.0140.062.0NaN40.12.303.1NaNNaN27.095.04.1NaN1.070RHYTHATR0000000010011
120003.06.04.00.0NaN14.139.336.0134.0118.040.0NaN27.42.512.3NaNNaN31.0168.00.4NaN0.068SEPSISUTI0000000000100
240101.03.01.00.0NaN12.735.133.0145.0120.046.07.4536.90.56NaN51.037.09.0145.0NaN100.01.077RESPARREST0000000010100
360003.06.05.00.0NaNNaN36.737.0NaN102.068.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.025ODSEDHYP0000000000000
480103.06.04.00.0NaN42.740.154.0133.0204.0198.07.4626.21.90NaN65.023.032.0145.0NaN21.00.082SEPSISPULM0000000010000
5101104.06.05.00.0NaN8.034.84.0NaN114.060.07.3925.9NaNNaN142.030.0NaN185.0NaN60.01.081S-VALVMI0000000110000
6120004.06.05.00.0NaN4.137.210.0142.0114.062.0NaN31.00.65NaNNaNNaN8.0121.0NaNNaN0.059S-FEMPGRAF0000000100000
7140004.06.05.00.0NaN6.036.618.0136.092.074.0NaN40.81.04NaNNaNNaN8.0289.0NaNNaN1.043ASTHMA0000000000000
8160004.06.05.00.0NaN10.936.635.0137.0113.0130.0NaN44.20.71NaNNaNNaN13.0156.0NaNNaN0.067CVASTROKE0000000000100
9180004.06.05.00.0NaNNaN37.44.0NaN103.063.0NaNNaNNaNNaNNaNNaNNaN215.0NaNNaN0.073SEPSISUTI0000000010100

Last rows

df_indexintubatedventdialysiseyesmotorverbalmedsurinewbctemperaturerespiratoryratesodiumheartratemeanbpphhematocritcreatininealbuminpao2pco2bunglucosebilirubinfio2genderageadmitdiagnosisaidshepaticfailurelymphomametastaticcancerleukemiaimmunosuppressioncirrhosiselectivesurgeryactivetxreadmitdiabetesactualicumortalityactualhospitalmortality
1485222970441104.06.05.00.01483.920012.336.830.0134.0112.064.07.41220.00.92NaN70.043.541.0221.0NaN30.01.066HYPOVOLEM0000000011000
1485232970461103.06.05.00.02217.9744NaN36.78.0NaN98.060.07.566NaNNaNNaN329.048.8NaN179.0NaN100.01.066PLEUREFFUS0000000011000
148524297048110NaNNaNNaN1.01328.832017.136.745.0142.0107.0116.07.33040.00.68NaN108.033.211.0172.0NaN45.01.051COMA0000000010000
1485252970501101.05.01.00.05039.97127.534.914.0143.062.0114.07.18526.00.732.8226.053.17.0182.00.2100.01.050S-CYSTOTH0000000110000
148526297052011NaNNaNNaN1.0NaNNaN36.711.0NaN68.0112.0NaNNaNNaNNaNNaNNaNNaN87.0NaNNaN1.079PLEUREFFUS0000000010111
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